Team APR

les 30 et 31 mai 2024

Université de Caen Normandie
Campus 2
Côte de nacre
Boulevard Maréchal Juin
14032 Caen Cedex 5

Tram ligne T2, arrêt Campus 2

Carte OpenStreetMap

• Jeudi 30 mai 2024
  • Arrivée en train suggérée à 12:03 en gare de Caen
  • 13:00-14:30: Déjeuner
  • 14:30-16:00: 1ère session, 3 exposés
  • 16:00-16:30: Pause
  • 16:30-18:00: 2ème session, 3 exposés
  • soir: dîner
• Vendredi 31 mai 2024
  • 09:00-10:30: 3ème session, 3 exposés
  • 10:30-11:00: Pause
  • 11:00-12:30: 4ème session, exposés, discussion sur l'enseignement
  • 12:30-14:00: Déjeuner
  • 14:00-15:00: Réunion APR
  • Départ suggéré par le train de 15:56 en gare de Caen

• Julien Courtiel (GREYC, Université de Caen)
• Amaury Curiel (APR, LIP6, Sorbonne Université)
• Matthieu Dien (GREYC, Université de Caen)
• Jean-Luc Lamotte (GREYC, Université de Caen)
• Ève Le Guillou (APR, LIP6, Sorbonne Université)
• Marco Milanese (APR, LIP6, Sorbonne Université)
• Mathieu Pont (APR, LIP6, Sorbonne Université)
• Keanu Sisouk (APR, LIP6, Sorbonne Université)
• Loïc Sylverstre (APR, LIP6, Sorbonne Université)
• Milla Valnet (APR, LIP6, Sorbonne Université)

Modular Counting of Linear Extensions
Matthieu Dien (GREYC, Université de Caen)

The counting of linear extensions is a prominent problem about partial orders. Unfortunately, the problem is computationally hard and in fact, relatively few counting procedures have been proposed in the literature. In this talk, we will present a new counting algorithm based on the modular decomposition of posets. This algorithm has a better parametrized complexity than the state of the art. Moreover, this approach leads us to consider a new parameter of posets (the BIT-width) with two corresponding conjectures.

Under-approximating Abstract Interpretation
Marco Milanese (APR, SU)

Static analysis by abstract interpretation has traditionally focused on program verification, that is on checking that programs are free of bugs. However, in practice it is not easy to achieve a low rate of false positives, and thus verification techniques are difficult to use to catch bugs. In this PhD we explore a different and unconventional analysis, based on abstract interpretation, allowing to compute under-approximations and thus catching bugs. This analysis infers sufficient pre-conditions for program defects, enabling developers to detect real bugs and obtain precise information on the conditions where they occur. Our work applies this analysis to the C programming languge: firstly, by focusing on numeric properties and more recently by adding support for the rest of the language (e.g., pointers, memory allocations). Finally, we discuss preliminary results of our experiments and future directions of work.